The Who-To-Follow System at Twitter: Strategy, Algorithms, and Revenue Impact
By Ashish Goel, Pankaj Gupta, J. Sirois, Aneesh Sharma, Siva Gurumurthy. 2015
31 citations recorded by the source corpus.
The who-to-follow system at Twitter is an algorithmic data product that recommends accounts for Twitter users to follow. Building the system involved algorithmic, analytics, operational, and experimental challenges; operations research and analytics techniques played a key role in resolving these challenges. This product has had significant direct impact on Twitter's growth and the quality of its user engagement, and has also been a major driver of revenue. More than one-eighth of all new connections on the Twitter network are a direct result of this system, and a substantial majority of Twitter's revenue comes from its promoted products, for which this system was a foundation. To place this contribution into perspective, Twitter is now a publicly traded company with a market capitalization of more than $30 billion, projected annual revenue of close to $1 billion, and more than 240 million active users.
Subfields: Computer Networks and Communications, Information Systems, Management Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
In Information Systems
- Jimmy J. Lin
- Jure Leskovec
- Aneesh Sharma
- Rodrigo Nogueira
- Zeynep Akkalyoncu Yilmaz
- Wei Yang
- Sebastian Hofstätter
- Allan Hanbury
In Management Information Systems
Citations in the corpus
- Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time (2017, background): “Perhaps, the closest to our work here is the “who to follow” systems at Twitter [14, 15] that places the entire follow graph in memory of a single machine and runs a personalized SALSA algorithm [19].”
- GraphJet: Real-Time Content Recommendations at Twitter (2016, background): “It has contributed substantially to enriching connections be-tween Twitter users [14] and it has inspired subsequent generations of systems within the organization.”